From 3cd3473bf7a3b41484baa86d9092248d78e7af39 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 21 四月 2023 17:17:37 +0800
Subject: [PATCH] docs
---
funasr/train/trainer.py | 34 ++++++++++++++++++++++++++++++----
1 files changed, 30 insertions(+), 4 deletions(-)
diff --git a/funasr/train/trainer.py b/funasr/train/trainer.py
index efe2009..7c187e9 100644
--- a/funasr/train/trainer.py
+++ b/funasr/train/trainer.py
@@ -94,7 +94,7 @@
wandb_model_log_interval: int
use_pai: bool
oss_bucket: Union[oss2.Bucket, None]
-
+ batch_interval: int
class Trainer:
"""Trainer having a optimizer.
@@ -186,7 +186,7 @@
logging.warning("No keep_nbest_models is given. Change to [1]")
trainer_options.keep_nbest_models = [1]
keep_nbest_models = trainer_options.keep_nbest_models
-
+
output_dir = Path(trainer_options.output_dir)
reporter = Reporter()
if trainer_options.use_amp:
@@ -560,12 +560,38 @@
# [For distributed] Because iteration counts are not always equals between
# processes, send stop-flag to the other processes if iterator is finished
iterator_stop = torch.tensor(0).to("cuda" if ngpu > 0 else "cpu")
-
+
+ #get the rank
+ rank = distributed_option.dist_rank
+ #get the num batch updates
+ num_batch_updates = 0
+ #ouput dir
+ output_dir = Path(options.output_dir)
+ #batch interval
+ batch_interval = options.batch_interval
+
start_time = time.perf_counter()
for iiter, (_, batch) in enumerate(
reporter.measure_iter_time(iterator, "iter_time"), 1
):
assert isinstance(batch, dict), type(batch)
+
+ if batch_interval > 0 and (not distributed_option.distributed or rank == 0):
+ if hasattr(model, "num_updates") or (hasattr(model, "module") and hasattr(model.module, "num_updates")):
+ num_batch_updates = model.get_num_updates() if hasattr(model,"num_updates") else model.module.get_num_updates()
+ if num_batch_updates % batch_interval == 0:
+ if options.use_pai and options.oss_bucket is not None:
+ buffer = BytesIO()
+ if hasattr(model, "module"):
+ torch.save(model.module.state_dict(), buffer)
+ else:
+ torch.save(model.state_dict(), buffer)
+ options.oss_bucket.put_object(os.path.join(output_dir, f"{num_batch_updates}step.pb"), buffer.getvalue())
+ else:
+ if hasattr(model, "module"):
+ torch.save(model.module.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
+ else:
+ torch.save(model.state_dict(), os.path.join(output_dir, f"{num_batch_updates}step.pb"))
if distributed:
torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
@@ -811,4 +837,4 @@
else:
if distributed:
iterator_stop.fill_(1)
- torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
\ No newline at end of file
+ torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)
--
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